Relapsed multiple myeloma demonstrates distinct patterns of immune microenvironment and malignant cell-mediated immunosuppression

Abstract

Immunotherapy has shown efficacy in relapsed multiple myeloma (MM). However, these therapies may depend on a functional tumor immune microenvironment (iTME) for their efficacy. Characterizing the evolution of the iTME over the disease course is necessary to optimize the timing of immunotherapies. We performed mass cytometry, cytokine analysis, and RNA sequencing on bone marrow samples from 39 (13 newly diagnosed [NDMM], 11 relapsed pre-daratumumab exposure [RMM], and 13 triple-refractory [TRMM]) MM patients. Three distinct cellular iTME clusters were identified; cluster 1 comprised mainly of NDMM and RMM patients; and clusters 2 and 3 comprised primarily of TRMM patients. We showed that naive T cells were decreased in clusters 2 and 3, cluster 2 was characterized by increased senescent T cells, and cluster 3 by decreased early memory T cells. Plasma cells in clusters 2 and 3 upregulated E2F transcription factors and MYC proliferation pathways, and downregulated interferon, TGF-beta, interleuking-6, and TNF-αlpha signaling pathways compared to cluster 1. This study suggests that the MM iTME becomes increasingly dysfunctional with therapy whereas the MM clone may be less dependent on inflammation-mediated growth pathways and less sensitive to IFN-mediated immunosurveillance. Our findings may explain the decreased sensitivity of TRMM patients to novel immunotherapies.

Fig. 1. Hierarchical clustering of patients based on immune subset frequencies (% of total CD45+ cells) identifies three distinct immune clusters.

Samples represent patients with newly diagnosed MM (NDMM), relapsed MM not refractory to anti-CD38 antibodies (RMM), and relapsed MM that are triple refractory (TRMM)

Fig. 2. Immune phenotype of cellular subsets.

The heat map highlights the marker expression within each unique cellular subset identified by mass cytometry

Fig. 3. Box plots showing the differential abundance of immune subsets.

The frequency of immune cell subsets within each iTME cluster are shown as the percentage of total CD45+ cells. For clarity, only subsets that were significantly different between (false discovery rate corrected p value of < 0.05) the three clusters are shown

Fig. 4. Differential abundance of T-cell phenotypes within each patient cluster.

CD4+ T-cell subsets are shown in (A), with the percentages of Tn (naïve T cells, comprising subsets T1 and T2), Tcm (central memory T cells, comprising of subsets T6 and T8), Tem (effector memory T cells, comprising of subsets T4, T7, T9), and Teff (effector T cells, comprising of the T10 subset). CD8+ T-cell subsets are shown in (B), with the percentages of Tn (naive T cells, comprising subsets T11 and T12), Tcm (central memory T cells, comprising of subsets T13, T14, T15), Tem (effector memory T cells, comprising ofsubsets T17, T18), and Teff (effector T cells, comprising of the subsets T16, T19, T20). Results are presented as mean ± standard error

Fig. 5. The mean normalized enrichment score (NES) of Hallmark pathways with FDR q value < 0.05 when comparing gene expression in the immune clusters 2 and 3 versus cluster 1 are represented by each bar.

NES < 0 represents downregulation of specified pathway in immune clusters 2 and 3, versus cluster 1. NES > 0 represents upregulation of specified pathway in immune clusters 2 and 3, versus cluster 1